Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
Generated from Trainer
dataset_size:505654
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use carnival13/all-mpnet-base-v2-modulepred with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use carnival13/all-mpnet-base-v2-modulepred with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("carnival13/all-mpnet-base-v2-modulepred") sentences = [ "module: stationery & printed material & services group: stationery & printed material & services supergroup: stationery & printed material & services example descriptions: munchkin crayons hween printedsheet mask 2 pk printed tape tour os silver butterfly relax with art m ab hardbacknotebook stickers p val youmeyou text heat w mandalorian a 5 nbook nediun bubble envelopes 6 pk whs pastel expan org p poll decoration 1 airtricity payasyoug", "retailer: groveify description: rainbow magicbooks", "retailer: crispcorner description: glazed k kreme", "retailer: vitalveg description: may held aop fl" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Normalize", | |
| "type": "sentence_transformers.models.Normalize" | |
| } | |
| ] |